文献类型：期刊文献

英文题名：Automatic image region annotation through segmentation based visual semantic analysis and discriminative classification

作者：Zhang, Jing[1]; Gao, Yongwei[1]; Feng, Shengwei[1]; Yuan, Yubo[1]; Lee, Chin-Hui[2]

机构：[1] Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, China; [2] School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States

年份：2016

卷号：2016-May

起止页码：1956

外文期刊名：ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

收录：EI(收录号：20162402489283)

语种：英文

摘要：We propose a new framework for automatic image annotation (AIA) of regions through segmentation based semantic analysis and discriminative classification. Given a test image, it is first segmented by a proposed texture-enhanced JSEG algorithm. Then these regions are represented by an extended bag-of-words model in which a feature vector, based on a visual lexicon with its vocabulary consisting of a visual word or a co-occurrence of multiple visual words, is constructed to represent the region content. Finally a concept classifier learned by a maximal figure-of-merit algorithm is used to predict the region labels. These models are discriminatively trained from image regions with multiple associations between regions and concepts. Experiments on a subset of the Corel 5K data set illustrate that our proposed approach to region AIA achieves more accurate annotation results than some sate-of-the-art algorithms. ? 2016 IEEE.